The present invention relates generally to automation of oilfield operations and more particularly to the use of General Bayesian Networks in the automation of oilfield operations.
Oilfield operations are data intensive operations with a myriad of measurements and functional relationships being available for observing and mathematically modeling, for example, conditions of operations, conditions of the operating environment, e.g., a wellbore, and conditions of equipment. Many sensors may be used to directly monitor the operations. As well as sensed conditions, other conditions may never be observed, either because it is impossible to directly measure such conditions, or because sensors are not available for a given job, i.e., the conditions are hidden.
Moreover, oilfield operations include processes and procedures that are often extremely expensive and dangerous. Equipment malfunctions may have dire effects both in terms of economic cost and in terms of operator and environmental safety. Inadequate interpretation of collected data may also cause the failure of achieving desired operational goals. It is therefore desirable to have tools for interpreting monitored oilfield data so as to provide accurate and timely feedback to control systems and operators both to optimize the results achieved and to avoid costly malfunctions.
One aspect of oilfield data is that often there are uncertainties in the data. Such uncertainties may reflect confidence in the measuring equipment, noise in the data or the like, thus, the measured data may be modeled as probability density functions reflecting the probability that certain variables have particular values.
Many functional relationships exist between properties of an oilfield operation, merely by way of example, surface weight on bit (SWOB) may equal the downhole weight on bit plus torsional friction.
Bayesian Networks are a tool that may be used to model systems that involve functional relationships between several variables modeled as probabilities of discrete states. A traditional Bayesian Network is a tool for estimating probabilities for discrete states of unknown variables of a system from functional relationships between the variables in the system. In such a network, the relationships between the variables are expressed as a table of conditional probabilities.
However, in addition to discrete states, many of the variables pertinent to wellbore procedures, including but not limited to drilling for oil or gas, are continuous-valued. Such would be the case for example for SWOB. In systems with continuous-valued variables, the value of a variable is a probability density function that is conditioned on the probability density functions of its parents and comes from a model that relates these variables. These models can take any form and are often not invertible. A Bayesian Network with continuous-valued variables is known as a General Bayesian Network.
The use of Bayesian Networks in oilfield operations has been described in U.S. Pat. Publ. No. 2007/0226158, “Bayesian Network Applications to Geology and Geophysics”. That patent application provides for data analysis using Bayesian Networks to describe variables and relationships between variables, including accounting for uncertainties in the data.
While certain variables in a system may be observed by taking measurements, other variables may be hidden from observation. The variables that are hidden may be functionally related to the variables with measurable values. The values of hidden variables may be as important to interpretation of an industrial operation as are the values of observed variables. It is therefore desirable to have a mechanism for determining values for the hidden variables.
In a General Bayesian Belief network, i.e., a Bayesian Belief Network with continuous-valued variables, it has hitherto been very difficult to estimate the values for hidden variables based on observed evidence.
From the foregoing it will be apparent that there is a need for a method of application of General Bayesian Networks to oilfield operations so that observed data may be used to draw inferences regarding other variables in oilfield operations, thereby providing tools useful in the automation of oilfield operations.